Digital educational technologies offer the potential to customize students'
experiences and learn what works for which students, enhancing the technology
as more students interact with it. We consider whether and when attempting to
discover how to personalize has a cost, such as if the adaptation to personal
information can delay the adoption of policies that benefit all students. We
explore these issues in the context of using multi-armed bandit (MAB)
algorithms to learn a policy for what version of an educational technology to
present to each student, varying the relation between student characteristics
and outcomes and also whether the algorithm is aware of these characteristics.
Through simulations, we demonstrate that the inclusion of student
characteristics for personalization can be beneficial when those
characteristics are needed to learn the optimal action. In other scenarios,
this inclusion decreases performance of the bandit algorithm. Moreover,
including unneeded student characteristics can systematically disadvantage
students with less common values for these characteristics. Our simulations do
however suggest that real-time personalization will be helpful in particular
real-world scenarios, and we illustrate this through case studies using
existing experimental results in ASSISTments. Overall, our simulations show
that adaptive personalization in educational technologies can be a double-edged
sword: real-time adaptation improves student experiences in some contexts, but
the slower adaptation and potentially discriminatory results mean that a more
personalized model is not always beneficial.

通过模拟实验，我们发现教育科技中的自适应个性化可能是一把双刃剑，实时个性化在某些情况下改善学生体验，但较慢的个性化和潜在的歧视结果意味着更个性化的模型并不总是有益的。